Object detection in image based on stochastic optimization

ABSTRACT

An electronic device includes circuitry that determines probability map information for a first image, based on application of a neural network model on the first image. The neural network model is trained to detect one or more objects based on a plurality of images associated with the one or more objects. The probability map information indicates a probability value for each pixel in the first image. A region corresponding to the one or more objects is detected in the first image based on the probability map information. A first set of sub-images is determined from the detected region, based on application of a stochastic optimization function on the probability map information. The one or more objects are detected from a second set of sub-images of the first set of sub-images, based on application of the neural network model on the second set of sub-images.

CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATION BY REFERENCE

None.

FIELD

Various embodiments of the disclosure relate to an object detection inan image. More specifically, various embodiments of the disclosurerelate to an object detection in image based on stochastic optimization.

BACKGROUND

With the advancements in a field of image processing, various techniquesfor detection of objects present in images have been developed. Thedetection of objects from the images may be used for various purposes.For example, license plates of vehicles may be detected from the imagesof road traffic, for surveillance and traffic regulation. In certainsituations of high definition (HD) images, the accurate detection ofobjects may be a computationally challenging task which further may beinefficient. In certain other situations, the size of the images isreduced to fasten the object detection tasks. However, in suchsituations, the accuracy of the object detection from the reduced-sizeimages may also reduce. Thus, an intelligent system may be requiredwhich may enhance the accuracy of object detection in an efficientmanner.

Further limitations and disadvantages of conventional and traditionalapproaches will become apparent to one of skill in the art, throughcomparison of described systems with some aspects of the presentdisclosure, as set forth in the remainder of the present application andwith reference to the drawings.

SUMMARY

An apparatus and a method for detection of an object in an image basedon stochastic optimization, and/or described in connection with, atleast one of the figures, as set forth more completely in the claims.

These and other features and advantages of the present disclosure may beappreciated from a review of the following detailed description of thepresent disclosure, along with the accompanying figures in which likereference numerals refer to like parts throughout.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram that illustrates an exemplary environment fordetection of an object in an image based on stochastic optimization, inaccordance with an embodiment of the disclosure.

FIG. 2 is a block diagram that illustrates an exemplary electronicdevice for detection of an object in an image based on stochasticoptimization, in accordance with an embodiment of the disclosure.

FIGS. 3A and 3B are diagrams that collectively illustrate an exemplaryscenario for detection of an object in an image based on stochasticoptimization, in accordance with an embodiment of the disclosure.

FIG. 4 is a diagram which depicts a flowchart that illustrates anexemplary method for detection of an object in an image based onstochastic optimization, in accordance with an embodiment of thedisclosure.

DETAILED DESCRIPTION

Various embodiments of the present disclosure may be found in anelectronic device and a method for accurate and efficient detection ofan object present in an image based on stochastic optimization. Theelectronic device may be configured to determine probability mapinformation for an image (for example a high-definition (HD) image),based on application of a neural network model on the image. The neuralnetwork model may be pre-trained to detect one or more objects based ona plurality of images (i.e. training dataset) associated with the one ormore objects. The probability map information may indicate a probabilityvalue for each pixel which may be associated with or may include aportion of the one or more objects captured in the original image. Theelectronic device may be further configured to detect a region (or anarea of interest) that may correspond to the one or more objects in theimage based on the determined probability map information of the image.The disclosed electronic device may further determine a first set ofsub-images (for example a set of to be cropped images) from the detectedregion, based on application of a stochastic optimization function onthe determined probability map information. The size of each of thefirst set of sub-images may be lesser than the size of the image.Further, the electronic device may detect the one or more objects (forexample license plate of a vehicle) from a second set of sub-images(i.e. a batch of the set of cropped images) of the first set ofsub-images, based on application of the neural network model on thesecond set of sub-images.

The disclosed electronic device may detect the one or more objects (suchlicense plates) from the image (for example HD image) based on thesecond set of sub-images which may be selected from the first set ofsub-images (i.e. which may be lesser in size than actual size of theimage (i.e. HD image)). The disclosed electronic device may furtherinput the selected second set of sub-images in batches to thepre-trained neural network model for detection of the one or moreobjects. Since the size of the sub-images, input in the batches to theneural network model, is lesser than that of the actual image (i.e. HDimage), the complexity for the object detection may be reduced. Further,the first set of sub-images may be determined from the region that mayinclude pixels that may have a higher probability (i.e. the probabilitymap information) of association with the one or more objects to bedetected. Thus, the disclosed electronic device may process only thepixels (in the region) which may have the higher probability ofinclusion or association with the objects and may not process otherpixels (with lower probability) to finally achieve an efficientobjection detection. Therefore, the selection of sub-images (i.e. havinghigher probability of inclusion of the objects) from the actual image(HD image), without re-sizing (i.e. reduction in size) the actual image,may further enhance or maintain the accuracy of the object detection bythe disclosed electronic device 102.

FIG. 1 is a block diagram that illustrates an exemplary environment fordetection of an object in an image based on stochastic optimization, inaccordance with an embodiment of the disclosure. With reference to FIG.1, there is shown a network environment 100 comprising an electronicdevice 102, an image capturing device 106, a communication network 108,and a server 110. The electronic device 102 may further include a neuralnetwork model 104. In some embodiments, the electronic device 102 may becommunicatively coupled to the image capturing device 106. In someembodiments, the image capturing device 106 may be integrated with theelectronic device 102. The electronic device 102 may be communicativelycoupled to the server 110, via the communication network 108. In FIG. 1,there is also shown a field-of-view 112 of the image capturing device106 and a first image 114 that may be captured by the image capturingdevice 106 based on the field-of-view 112 of the image capturing device106. The first image 114 may include a plurality of objects 116A-116D(collectively referred herein as the plurality of objects 116). Theplurality of objects 116 may include a first object 116A, a secondobject 116B, a third object 116C, and a fourth object 116D. For example,each object from the plurality of objects 116 may correspond to avehicle. Further, each object from the plurality of objects 116 mayinclude a vehicle license plate of a respective vehicle such as, a firstvehicle license plate 118A, a second vehicle license plate 1188, a thirdvehicle license plate 118C, and a fourth vehicle license plate 118D.

It may be noted that the vehicles 116A-116D and vehicle license plates118A-118D shown in FIG. 1 are presented merely as examples of theplurality of objects 116. The present disclosure may be also applicableto other types of objects such as human faces, humans, animals, otheranimate or inanimate objects. A description of other types of objectshas been omitted from the disclosure for the sake of brevity. It may befurther noted here that the position, orientation, arrangement, and/orshape of the plurality of objects 116 shown in FIG. 1 is presentedmerely as an example. The present disclosure may be also applicable toother positions, orientations, arrangements, and/or shapes of theplurality of objects 116, without deviation from the scope of thedisclosure. The four number of objects in the plurality of objects 116shown in FIG. 1 is also presented merely as an example. The plurality ofobjects 116 may include a N number of objects which may be lesser orgreater than four for the purpose of the disclosure, without deviationfrom the scope of the disclosure.

The electronic device 102 may include suitable logic, circuitry,interfaces, and/or code that may be configured to detect one or moreobjects in an image (for example the first image 114). For the detectionof the plurality of objects 116, the electronic device 102 may beconfigured to apply the neural network model 104 on the first image 114and determine probability map information for the first image 114. Theprobability map information may indicate a probability value for eachpixel associated with the plurality of objects 116 in the first image114. Further, the electronic device 102 may be configured to detect aregion that corresponds to the plurality of objects 116 in the firstimage 114 based on the determined probability map information of thefirst image 114. The electronic device 102 may be configured todetermine a first set of sub-images from the detected region, based onapplication of a stochastic optimization function on the determinedprobability map information and further detect the plurality of objects116 from a second set of sub-images of the first set of sub-images,based on application of the neural network model 104 on the second setof sub-images. Examples of the electronic device 102 may include, butare not limited to a vehicle tracker device, an Automatic License PlateRecognition (ALPR) device, an in-vehicle embedded device, an electroniccontrol unit (ECU), a handheld computer, a cellular/mobile phone, atablet computing device, a Personal Computer (PC), a mainframe machine,a server, and other computing devices.

In one or more embodiments, the neural network model 104 may includeelectronic data, such as, for example, a software program, code of thesoftware program, libraries, applications, scripts, or other logic orinstructions for execution by a processing device, such as a processorof the electronic device 102. The neural network model 104 may includecode and routines configured to enable a computing device, such as theprocessor of the electronic device 102, to perform one or moreoperations. The one or more operations may include classification ofeach pixel of an image (e.g., the first image 114) into one of a truedescription or a false description associated with the plurality ofobjects 116. Additionally or alternatively, the neural network model 104may be implemented using hardware including a processor, amicroprocessor (e.g., to perform or control performance of one or moreoperations), a field-programmable gate array (FPGA), or anapplication-specific integrated circuit (ASIC). In some other instances,the neural network model 104 may be implemented using a combination ofhardware and software. Examples of the neural network model 104 mayinclude, but are not limited to, an artificial neural network (ANN), aconvolutional neural network (CNN), a CNN-recurrent neural network(CNN-RNN), Region-CNN (R-CNN), Fast R-CNN, Faster R-CNN, a Long ShortTerm Memory (LSTM) network based RNN, a combination of CNN and ANN, acombination of LSTM and ANN, a gated recurrent unit (GRU)-based RNN, adeep Bayesian neural network, a Generative Adversarial Network (GAN), adeep learning based object detection model, a feature-based objectdetection model, an image segmentation based object detection model, ablob analysis-based object detection model, a “you look only once”(YOLO) object detection model, or a single-shot multi-box detector (SSD)based object detection model. In some embodiments, the neural networkmodel 104 may conduct numerical computation techniques using data flowgraphs. In certain embodiments, the neural network model 104 may bebased on a hybrid architecture of multiple Deep Neural Networks (DNNs).

The image capturing device 106 may include suitable logic, circuitry,interfaces, and/or code that may be configured to capture one or moreimage frames, such as, the first image 114 based on the field of view112 of the image capturing device 106. Examples of the first image 114may include High Dynamic Range (HDR) images, High Definition (HD) image,4K resolution image (such as 3840×2160 resolution image, 4096×2160resolution image, 7680×4320 resolution image), or a HD RAW image. Insome embodiments, the first image 114 may be an image frame of videocontent captured by the image capturing device 106. The image capturingdevice 106 may be configured to communicate the captured image frames(e.g., the first image 114) as input to the electronic device 102. Theimage capturing device 106 may be implemented by use of a charge-coupleddevice (CCD) technology or complementary metal-oxide-semiconductor(CMOS) technology. Examples of the image capturing device 106 mayinclude, but are not limited to, an image sensor, a wide-angle camera,an HD camera, a front camera, a driving camera, a 360 degree camera, aclosed circuitry television (CCTV) camera, a stationary camera, anaction-cam, a video camera, a camcorder, a digital camera, a cameraphone, a time-of-flight camera (ToF camera), a night-vision camera,and/or other image capture devices. The image capturing device 106 maybe implemented as an integrated unit of the electronic device 102 or asa separate device (e.g., a camera device mounted on the electronicdevice 102).

The communication network 108 may include a medium through which theelectronic device 102 may communicate with the server 110. In someembodiments, the electronic device 102 may communicate with the imagecapturing device 106 via the communication network 108. Examples of thecommunication network 108 may include, but are not limited to, theInternet, a cloud network, a Long Term Evolution (LTE) network, aWireless Local Area Network (WLAN), a Local Area Network (LAN), atelephone line (POTS), or other wired or wireless network. Variousdevices in the network environment 100 may be configured to connect tothe communication network 108, in accordance with various wired andwireless communication protocols. Examples of such wired and wirelesscommunication protocols may include, but are not limited to, at leastone of a Transmission Control Protocol and Internet Protocol (TCP/IP),User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), FileTransfer Protocol (FTP), ZigBee, EDGE, IEEE 802.11, light fidelity(Li-Fi), 802.16, IEEE 802.11s, IEEE 802.11g, multi-hop communication,wireless access point (AP), device to device communication, cellularcommunication protocols, or Bluetooth (BT) communication protocols, or acombination thereof.

The server 110 may include suitable logic, circuitry, interfaces, and/orcode that may be configured to train one or more neural network models,for example, recurrent neural network (RNN), such as Long Short TermMemory networks (LSTM) networks, CNN, deep neural network, or anartificial neural network that may be a combination of the ANN and CNNnetworks. For example, a first neural network model may be trained forvehicle region detection from a plurality of images, and a secondnetwork model may be trained for license plate region detection from thedetected vehicle region included in the plurality of images. The server110 may be configured to deploy the trained model(s) on the electronicdevice 102 for real time or near real time vehicles and license platesdetection and/or recognition. In some embodiments, the server 110 may beconfigured to store the capture first image 114 and the plurality ofobjects 116 (or the license plates) detected from the first image 114.Examples of the server 110 may include, but are not limited to, anapplication server, a cloud server, a web server, a database server, afile server, a mainframe server, or a combination thereof.

In operation, the server 110 may be configured to train the neuralnetwork model 104 for detection of an object from an image (for examplethe first image 114). The neural network model 104 may be pre-trainedbased on a plurality of images that may include one or more objects. Theserver 110 may transmit the trained neural network model 104 to theelectronic device 102 and deploy the trained neural network model 104 onthe electronic device 102. The electronic device 102 may be configuredto store the received neural network model 104 in a memory (e.g., amemory 206 of FIG. 2) of the electronic device 102, and apply thetrained neural network model for object detection from images. Examplesof the neural network model 104 may include, but are not limited to, anartificial neural network (ANN), a convolutional neural network (CNN), aCNN-recurrent neural network (CNN-RNN), Region-CNN (R-CNN), Fast R-CNN,Faster R-CNN, a Long Short Term Memory (LSTM) network based RNN, acombination of CNN and ANN, a combination of LSTM and ANN, a gatedrecurrent unit (GRU)-based RNN, a deep Bayesian neural network, aGenerative Adversarial Network (GAN), a deep learning based objectdetection model, a feature-based object detection model, an imagesegmentation based object detection model, a blob analysis-based objectdetection model, a “you look only once” (YOLO) object detection model,or a single-shot multi-box detector (SSD) based object detection model.

In accordance with an embodiment, the electronic device 102 may beconfigured to control the image capturing device 106 to capture asequence of image frames (that may include a plurality of differentvehicles) in the field-of-view 112 of the image capturing device 106. Inone example, the sequence of image frames may be a live video (e.g., avideo including the first image 114) of a road portion that may includethe plurality of different vehicles, such as, the first object 116A orthe second object 1168. Examples of the first object 116A or the secondobject 1168 (as vehicles) may include, but is not limited to, a car, amotorcycle, a truck, a bus, or other vehicles with license plates. Insome embodiments, the electronic device 102 may control the imagecapturing device 106 to capture the first image 114. The first image 114may include, for example, the High Dynamic Range (HDR) image, HighDefinition (HD) image, or 4K resolution image.

In accordance with an embodiment, the electronic device 102 may befurther configured to detect one or more bounding boxes in the firstimage 114 based on the application of the neural network model 104 onthe first image 114. Each of the one or more bounding boxes may includethe plurality of objects 116 included in the first image 114. Thedetection of the one or more bounding boxes in the first image 114 aredescribed, for example, in FIG. 3A. The electronic device 102 may befurther configured to determine probability map information for thefirst image 114, based on application of the neural network model 104 onthe first image 114 or based on the detected one or more bounding boxesin the first image 114. The probability map information may indicate aprobability value for each pixel associated with the plurality ofobjects 116 in the first image 114. In some embodiments, the probabilitymap information may indicate a presence or absence of a portion of anobject from the plurality of objects 116 at each pixel in the firstimage 114. The probability map information is described, for example, inFIG. 3A.

The electronic device 102 may be further configured to detect the regionthat corresponds to the plurality of objects 116 in the first image 114based on the determined probability map information of the first image114 as described, for example, in FIG. 3A. The electronic device 102 mayfurther determine a first set of sub-images from the detected region,based on application of a stochastic optimization function on thedetermined probability map information. The first set of sub-images maybe cropped images from the first image 114. Therefore, the size of eachof the first set of sub-images may be lesser than the size of the firstimage 114. Examples of the stochastic optimization function include, butare not limited to, a cost function, a direct search function, asimultaneous perturbation function, a Hill climbing function, a randomsearch function, a Tabu search function, a Particle Swarm Optimization(PSO) function, an Ant Colony Optimization function, a simulatedannealing function, or a genetic function. Further, the electronicdevice 102 may be configured to detect the plurality of objects 116 froma second set of sub-images of the first set of sub-images, based onapplication of the neural network model 104 on the second set ofsub-images. The second set of sub-images may be selected from the firstset of sub-images in batches. The number of second set of sub-imagesselected in batches from the first set of sub-images may be predefinedor stored in the memory 206. The detection of the plurality of objects116 from the second set of sub-images is described, for example, in FIG.3B.

FIG. 2 is a detailed block diagram that illustrates an electronic devicefor detection of an object in an image based on stochastic optimization,in accordance with an embodiment of the disclosure. FIG. 2 is explainedin conjunction with elements from FIG. 1. With reference to FIG. 2,there is shown a block diagram 200 that depicts an electronic device,such as, the electronic device 102. The electronic device 102 mayinclude circuitry 202 that comprises one or more processors, such as, aprocessor 204. The electronic device 102 may further include a memory206, an input/output (I/O) device 208, and a network interface 212. Thememory 206 may include the neural network model 104. Further, the I/Odevice 208 of the electronic device 102 may include a display device210. The network interface 212 may communicatively couple the electronicdevice 102 with the communication network 108, to connect the electronicdevice 102 with the server 110.

The circuitry 202 may comprise suitable logic, circuitry, interfaces,and/or code that may be configured to execute a set of instructionsstored in the memory 206. The circuitry 202 may be configured to executethe set of instructions associated with different operations to beexecuted by the electronic device 102. For example, some of theoperations may include determination of the probability map informationfor the first image 114 based on application of the neural network model104 on the first image 114, detection of the region based on thedetermined probability map information of the first image 114,determination of the first set of sub-images from the detected region,and detection of the plurality of objects 116 from the second set ofsub-images of the first set of sub-images, based on application of theneural network model 104 on the second set of sub-images. The circuitry202 may include one or more specialized processing units, which may beimplemented as a separate processor. In an embodiment, the one or morespecialized processing units may be implemented as an integratedprocessor or a cluster of processors that perform the functions of theone or more specialized processing units, collectively. The circuitry202 may be implemented based on a number of processor technologies knownin the art. Examples of implementations of the circuitry 202 may be anX86-based processor, a Graphics Processing Unit (GPU), a ReducedInstruction Set Computing (RISC) processor, an Application-SpecificIntegrated Circuit (ASIC) processor, a Complex Instruction Set Computing(CISC) processor, a microcontroller, a central processing unit (CPU),and/or other control circuits.

The processor 204 may comprise suitable logic, circuitry, and interfacesthat may be configured to execute instructions stored in the memory 206.In certain scenarios, the processor 204 may be configured to execute theaforementioned operations of the circuitry 202. The processor 204 may beimplemented based on a number of processor technologies known in theart. Examples of the processor 204 may be a Central Processing Unit(CPU), X86-based processor, a Reduced Instruction Set Computing (RISC)processor, an Application-Specific Integrated Circuit (ASIC) processor,a Complex Instruction Set Computing (CISC) processor, a GraphicalProcessing Unit (GPU), other processors, or a combination thereof.

The memory 206 may comprise suitable logic, circuitry, interfaces,and/or code that may be operable to store a set of instructionsexecutable by the circuitry 202 or the processor 204. The memory 206 maybe configured to store a sequence of image frames (e.g., the first image114) captured by the image capturing device 106. The memory 206 may beconfigured to store the neural network model 104 that may be pre-trainedto detect the plurality of objects 116 from an image (such as the firstimage 114). Examples of implementation of the memory 206 may include,but are not limited to, Random Access Memory (RAM), Read Only Memory(ROM), Hard Disk Drive (HDD), and/or a Secure Digital (SD) card.

The I/O device 208 may comprise suitable logic, circuitry, interfaces,and/or code that may be configured to receive an input and provide anoutput based on the received input. The I/O device 208 may includevarious input and output devices, which may be configured to communicatewith the circuitry 202. Examples of the I/O device 208 may include, butare not limited to, a touch screen, a keyboard, a mouse, a joystick, adisplay device (for example, the display device 210), a microphone, anda speaker. The display device 210 may comprise suitable logic,circuitry, and interfaces that may be configured to display an output ofthe electronic device 102. The display device 210 may be configured todisplay identification information (for example name or detected vehicleplate number) of the detected plurality of objects 116 in the firstimage 114. In some embodiments, the display device 210 may be anexternal display device associated with the electronic device 102. Thedisplay device 210 may be a touch screen which may enable a user toprovide a user-input via the display device 210. The touch screen may beat least one of a resistive touch screen, a capacitive touch screen, ora thermal touch screen. The display device 210 may be realized throughseveral known technologies such as, but not limited to, at least one ofa Liquid Crystal Display (LCD) display, a Light Emitting Diode (LED)display, a plasma display, or an Organic LED (OLED) display technology,or other display devices. In accordance with an embodiment, the displaydevice 210 may refer to a display screen of a head mounted device (HMD),a smart-glass device, a see-through display, a projection-based display,an electro-chromic display, or a transparent display.

The network interface 212 may comprise suitable logic, circuitry,interfaces, and/or code that may be configured to enable communicationbetween the electronic device 102, and the server 110 via thecommunication network 108. The network interface 212 may implement knowntechnologies to support wired or wireless communication with thecommunication network 108. The network interface 212 may include, but isnot limited to, an antenna, a frequency modulation (FM) transceiver, aradio frequency (RF) transceiver, one or more amplifiers, a tuner, oneor more oscillators, a digital signal processor, a coder-decoder (CODEC)chipset, a subscriber identity module (SIM) card, and/or a local buffer.The network interface 212 may communicate via wireless communicationwith networks, such as the Internet, an Intranet and/or a wirelessnetwork, such as a cellular telephone network, a wireless local areanetwork (LAN) and/or a metropolitan area network (MAN). The wirelesscommunication may use any of a plurality of communication standards,protocols and technologies, such as Long Term Evolution (LTE), GlobalSystem for Mobile Communications (GSM), Enhanced Data GSM Environment(EDGE), wideband code division multiple access (W-CDMA), code divisionmultiple access (CDMA), time division multiple access (TDMA), Bluetooth,Wireless Fidelity (Wi-Fi) (e.120g., IEEE 802.11a, IEEE 802.11b, IEEE802.11g and/or IEEE 802.11n), voice over Internet Protocol (VoIP),Wi-MAX, a protocol for email, instant messaging, and/or Short MessageService (SMS).

FIGS. 3A and 3B collectively, illustrate an exemplary scenario fordetection of an object in an image based on stochastic optimization, inaccordance with an embodiment of the disclosure. FIGS. 3A and 3B areexplained in conjunction with elements from FIG. 1 and FIG. 2. Withreference to FIGS. 3A and 3B, there are shown scenarios 300A and 300B(collectively a scenario 300) that depict a processing pipeline of thefirst image 114 executed by the electronic device 102 or the circuitry202 using the neural network model 104.

With reference to FIG. 3A, at 302, an image-capture operation may beexecuted. In the image-capture operation, an image-capturing device (forexample the image capturing device 106) may be controlled by thecircuitry 202 of the electronic device 102 to capture the first image114 in the field-of-view 112 of the image capturing device 106. Inaccordance with an embodiment, the first image 114 may be of a firstsize. For instance, the first image 114 may be a High-Definition (HD)image or a 4K resolution image (for example with 3840*2160 pixel-size or4096*2160 pixel-size as the first size. The image capturing device 106may be configured to transmit the first image 114 to the electronicdevice 102. In some embodiments, the electronic device 102 may controlthe image capturing device 106 to capture and transmit a sequence ofimages which may include first image 114. The electronic device 102 maybe configured to regularly control the image capturing device 106 at aparticular time interval (for example in every few seconds or minutes)to capture and provide the first image 114. The captured first image 114may include the plurality of objects 116 (for example vehicles).

At 304, a bounding-box detection operation may be executed. In thebounding-box detection operation, the circuitry 202 of the electronicdevice 102 may be configured to apply the neural network model 104 onthe captured first image 114 to detect one or more bounding boxes in thefirst image 114. Each of the one or more detected bounding boxes mayinclude one or more of the plurality of objects 116. For example, asshown in FIG. 3A, the plurality of objects 116 may include four vehicles116A-116D that may include respective vehicle license plates 118A-118D.Based on application of the neural network model 104 on the first image114, the circuitry 202 may detect one or more bounding boxes that mayinclude a first bounding box 314A, a second bounding box 314B, a thirdbounding box 314C, and a fourth bounding box 314D. Each of the fourdetected one or more bounding boxes 314A-314D may encompass an image ofeach of the respective vehicles 116A-116D. The neural network model 104may be pre-trained to detect the bounding boxes around the plurality ofobjects 116 captured in the first image 114. Examples of the neuralnetwork model 104 used for the bounding-box detection operation mayinclude, but are not limited to, an artificial neural network (ANN), aconvolutional neural network (CNN), a CNN-recurrent neural network(CNN-RNN), Region-CNN (R-CNN), Fast R-CNN, Faster R-CNN, a Long ShortTerm Memory (LSTM) network based RNN, a combination of CNN and ANN, acombination of LSTM and ANN, a gated recurrent unit (GRU)-based RNN, adeep Bayesian neural network, a Generative Adversarial Network (GAN), adeep learning based object detection model, a feature-based objectdetection model, an image segmentation based object detection model, ablob analysis-based object detection model, a “you look only once”(YOLO) object detection model, or a single-shot multi-box detector (SSD)based object detection model.

At 306, a probability map determination operation may be executed. Inthe probability map determination operation, the circuitry 202 of theelectronic device 102 may be configured to determine the probability mapinformation for the first image 114, based on the one or more boundingboxes which may be detected based on the application of the neuralnetwork model 104 on the first image 114. The probability mapinformation may indicate a probability value for each pixel of the firstimage 114 associated with the plurality of objects 116 in the firstimage 114. In some embodiments, the probability map information mayindicate an absence or presence of a portion of an object from theplurality of objects 116 at each pixel in the first image 114. In someembodiments, the circuitry 202 may determine the probability mapinformation for all the pixels included in the first image 114. In otherembodiments, the circuitry 202 may determine the probability mapinformation for the pixels included in the detected bounding boxes314A-314D to save time or enhance a speed of the object detection.

In FIG. 3A, there is also shown exemplary pixel locations in the firstimage 114, such as, pixels P1 316A, P2 316B, P3 316C, P4 316D, P5 316E,P6 316F, and P7 316G. The probability map information determined for thefirst image 114 may include probability values indicative of anassociation of each pixel (including the pixels P1 316A to P7 316G) withthe plurality of objects 116 in the first image 114. The probabilityvalues of pixels within the detected bounding box may be higher that theprobability values of the rest of the pixels in the first image. Forexample, the probability values of the pixels P1 316A, P3 316C, P5 316E,and P7 316G that lie within the detected bounding boxes 314A-314D,respectively, may be higher than that of the pixels outside the detectedbounding boxes, such as, the pixels P2 316B, P4 316D, and P6 316F. Forexample, the probability value for the pixel P6 316F (which lies outsidethe detected bounding boxes 314A-314D) may be 0.45 or less, and theprobability value for the pixel P7 316G (which lies within the fourthbounding box 314D and may be associated with or include the fourthvehicle 116D as object) may be 0.75 or more. In case of vehicle licenseplates as objects, the probability values for pixels, associated with orwhich may include portions of the vehicle license plates, may be higherthan the probability values for the rest of the pixels in the firstimage 114. Since the license plates for vehicles are typically locatedat a lower middle end of the vehicle (or at a lower portion of thebounding box), as shown in FIG. 3A, the pixel P1 316A that lie towardslower middle end of the first bounding boxes 314A, may have higherprobability value, for example as, 0.85 or more. In some embodiments,the probability map information may be in matrix form which may indicatethe determined probability value for each pixel, where the probabilityvalue may indicate that the corresponding pixel is associated with thedetected bounding boxes 314A-314D or may include information of theplurality of objects 116 included in the first image 114. In someembodiments, the probability map information may form a heat map basedon the determined probability values for each pixel in the first image114.

In accordance with an embodiment, one or more imaging parameters may beassociated with the image capturing device 106. Examples of the one ormore imaging parameters may include, but are not limited to, a positionparameter associated with the image capturing device 106, an orientationparameter associated with the image capturing device 106, a zoomingparameter associated with the image capturing device 106, a type of animage sensor associated with the image capturing device 106, a pixelsize associated with the image sensor of the image capturing device 106,a lens type associated with the image capturing device 106, a focallength associated with the image capturing device 106 to capture thefirst image 114, or a geo-location of the image capturing device 106. Insome embodiments, the circuitry 202 may be configured to detect a changein an imaging parameter associated with the image capturing device 106.In case of detected change in the one of the imaging parameters, thecircuitry 202 may be further configured to again determine or update theprobability map information based on the detected change in the imagingparameter. In accordance with an embodiment, the circuitry 202 may use aKalman Filter to determine or update the probability map information.The circuitry 202 may again determine or update the probability mapinformation since the change in the imaging parameter (for examplechange in zooming, orientation, linear position, focal length, or GPSlocation) of the image capturing device 106 may change the plurality ofobjects 116 or the detected bounding boxes 314A-314D in the first image114 captured again based on the changed imaging parameter. The changedimaging parameter may change a number, size, or position of theplurality of objects 116 in the first image 114 captured again by theimage capturing device 106 with the change in one or more imagingparameters.

In accordance with an embodiment, the circuitry 202 may again determineor update the probability map information based on change of a relativepixel positioning, pixel size, or resolution, associated with theplurality of objects 116 in images captured by the image capturingdevice 106. For example, the probability map information may be updatedbased on a change in an object's position or pixel size in a currentimage as compared to the object's position or size in a previouslycaptured image. Further, the relative position or pixel size of theplurality of objects 116 may vary with change in the geo-location orphysical position of the image capturing device 106 and/or theelectronic device 102. For example, an image captured on a street or acity may have different position (or pixel size) of vehicles/licenseplates (as object) as compared to another image captured on a highway.Further, a position, an orientation, or a zooming parameter (e.g., anoptical-zoom level) of the image capturing device 106 may also influencethe relative pixel position, pixel size or number of the plurality ofobjects 116 in the captured first image 114. In addition, hardwareimage-capturing parameters of the image capturing device 106 mayinfluence pixel position, pixel size or pixel resolution associated withthe plurality of objects 116 in the first image 114. Examples of thehardware image-capturing parameters of the image capturing device 106may include, but are not limited to, a type of an image sensor, a lenstype, or a focal length, associated with the image capturing device 106to capture the first image 114.

In some embodiments, the circuitry 202 may detect a change in ambientconditions (for example light or brightness) around the image capturingdevice 106 and again determine or update the probability map informationbased on the detected ambient conditions. For example, a change inlighting/brightness around the image capturing device 106 may changebrightness information of the pixels associated with the plurality ofobjects 116 captured in the first image 114. The change in thebrightness information may further affect the detection of the pluralityof objects 116 from the first image 114. For example, during day time(or during sunny day), the plurality of objects 116 captured in thefirst image 114 may be brighter, than the plurality of objects 116captured during evening time (or during rainy day). Thus, the disclosedelectronic device 102 may be configured to again determine or update theprobability map information based on detected change in the ambientconditions (as the imaging parameters) around the image capturing device106.

In some embodiments, the circuitry 202 may be configured to determinethe probability map information at a defined time interval. Forinstance, the circuitry 202 may be configured to control the imagecapturing device 106 to capture a second image (not shown in FIG. 1) atthe defined time interval after the capture of the first image 114.Thereafter, the circuitry 202 may be configured to determine theprobability map information for the captured second image, as describedat step 306 of FIG. 3A. Thus, the disclosed electronic device 102 maydynamically update or determine the probability map information of thefirst image 114 based on the change in the first image 114 or areal-time change in one of the imaging parameters associated with theimage capturing device 106. This dynamic adaptability to the change inthe imaging parameter (or imaging conditions) of the image capturingdevice 106 may further enhance the accuracy of the object detectionirrespective of any change in the imaging condition (for examplezooming, different geo-locations, two-dimensional or three-dimensionalmovement, ambient lighting, or image sensor type/pixel size) of theimage capturing device 106.

With reference to FIG. 3B, at 308, a region detection operation isexecuted. In the region detection operation, the circuitry 202 of theelectronic device 102 may be further configured to detect a region 318that may correspond to the plurality of objects 116 in the first image114 based on the determined probability map information. In someembodiments, the circuitry 202 may consider a predefined probabilitythreshold for the detection of the region 318 based on the determinedprobability map information, for each pixel in the first image 114. Forexample, all the pixels may be considered in the region 318 for whichthe determined probability value in the probability map informationexceeds predefined probability threshold (for example 0.7). The detectedregion 318 in the first image 114 may indicate a maximum coverage orinclusion of the plurality of objects 116 of the first image 114, asshown in FIG. 3B. In another embodiment, to detect the region 318, thecircuitry 202 may apply different image processing techniques such as,but not limited to, a region-growing method, an edge detectiontechnique, an image segmentation technique, a boundary detectiontechnique, a fuzzy rule-based thresholding, a maximum entropy method, abalanced histogram thresholding, a hybrid thresholding, Otsu's method(maximum variance method), or k-means clustering. In some embodiments,the region 318 may encompass the one or more detected bounding boxes314A-314D or include at least a portion of each of the one or moredetected bounding boxes 314A-314D as shown in FIG. 3B.

At 310, a stochastic optimization operation is executed. In thestochastic optimization operation, the circuitry 202 of the electronicdevice 102 may be configured to determine a first set of sub-images320A-320D from the detected region 318 based on application of astochastic optimization function on the determined probability mapinformation. Examples of the stochastic optimization function mayinclude, but are not limited to, a cost function, a direct searchfunction, a simultaneous perturbation function, a Hill climbingfunction, a random search function, a Tabu search function, a ParticleSwarm Optimization (PSO) function, an Ant Colony Optimization function,a simulated annealing function, or a genetic function. In an embodiment,based on the application of the stochastic optimization function on thedetermined probability map information, the circuitry 202 may beconfigured to determine a number of sub-images of the first set ofsub-images 320A-320D. Further, the circuitry 202 may also be configuredto determine a second size of each of the first set of sub-images320A-320D and a position of each of the first set of sub-images320A-320D in the first image 114 based on the application of thestochastic optimization function on the determined probability mapinformation.

The first set of sub-images 320A-320D determined from the region 318 mayinclude a first sub-image 320A, a second sub-image 320B, a thirdsub-image 320C, and a fourth sub-image 320D within the first boundingbox 314A, the second bounding box 314B, the third bounding box 314C, andthe fourth bounding box 314D, respectively. In an embodiment, the secondsize of each of the first set of sub-images 320A-320D (i.e. to becropped images) may be lesser than the first size of the first image 114(i.e. HD image or 4K image). For instance, the first image 114 may be of4K resolution with a pixel size of 3840*2160 or 4096*2160, and the firstsub-image 320A and the fourth sub-image 320D may be of pixel size of128*128, while the second sub-image 320B and the third sub-image 320Cmay be of pixel size of 256*256. In some embodiments, the pixel size ofone or more of the first set of sub-images 320A-320D may be, forexample, 512*512. The reduced pixel size of the first set of sub-images320A-320D (to be cropped) in comparison to pixel size of the first image114 (i.e. originally captured) may further reduce complexity or enhanceefficiency of the object detection by the disclosed electronic device102. In accordance with an embodiment, the first set of sub-images320A-320D of the plurality of objects 116 may include images of thevehicle license plates (such as the first vehicle license plate 118A,the second vehicle license plate 118B). In accordance with anembodiment, the circuitry 202 may be configured to again determine thefirst set of sub-images 320A-320D from the detected region 318 based onthe detected change in the one or more imaging parameters (e.g. zooming,position, geo-location, ambient condition, image sensor) associated withthe image capturing device 106.

At 312, an object detection operation is executed. In the objectdetection operation, the circuitry 202 of the electronic device 102 maybe configured to detect the one or more objects (e.g., the one or morevehicle license plates 118A-118D) in the first image 114. To detect theone or more objects, the circuitry 202 may be configured to select asecond set of sub-images from the first set of sub-images 320A-320D. Thecircuitry 202 may be configured to crop the detected first set ofsub-images 320A-320D from the first image 114 and further select thesecond set of sub-images from the cropped first set of sub-images320A-320D. The circuitry 202 may be further configured to apply theneural network model 104 on the selected second set of sub-images todetect the one or more objects (e.g., the one or more vehicle licenseplates 118A-118D) from the first image 114. Examples of the neuralnetwork model 104 used for the object detection operation may include,but are not limited to, an artificial neural network (ANN), aconvolutional neural network (CNN), a CNN-recurrent neural network(CNN-RNN), Region-CNN (R-CNN), Fast R-CNN, Faster R-CNN, a Long ShortTerm Memory (LSTM) network based RNN, a combination of CNN and ANN, acombination of LSTM and ANN, a gated recurrent unit (GRU)-based RNN, adeep Bayesian neural network, a Generative Adversarial Network (GAN), adeep learning based object detection model, a feature-based objectdetection model, an image segmentation based object detection model, ablob analysis-based object detection model, a “you look only once”(YOLO) object detection model, or a single-shot multi-box detector (SSD)based object detection model.

In an embodiment, the circuitry 202 may select the second set ofsub-images from the first set of sub-images 320A-320D based on abatching criteria associated with the neural network model 104. In anembodiment, the trained neural network model 104 may be fed with one ormore batches of the second set of sub-images for further objectdetection. In some embodiments, the number of second set of sub-imagesin each of the batches may be equal, for example, a batch-size of 4sub-images, 5-sub-images, or 16 sub-images. In some embodiment, thecircuitry 202 may be configured to determine the batch size based on thenumber of the first set of sub-images 320A-320D or the number ofbounding boxes detected in the first image 114. In some embodiments, thebatch size may be predefined. In another embodiment, a pixel size ofeach sub-image (i.e. in the first set of sub-images 320A-320D) selectedin one batch may be same. For example, the first sub-image 320A and thefourth sub-image 320D (i.e. each of a pixel size 128*128) may beselected in a first batch of sub-images that may correspond to thesecond set of sub-images for a first iteration of the object detectionoperation. Similarly, the second sub-image 320B and the third sub-image320C (i.e. each of a pixel size 256*256) may be selected in a secondbatch of sub-images that may correspond to the second set of sub-imagesfor a second iteration of the object detection operation. In anotherembodiment, the circuitry 202 may form batches of the second set ofsub-images based on an order of detection of the bounding boxes314A-314D or the first set of sub-images 320A-320D. For example, thefirst sub-image 320A and the second sub-image 320B may form the firstbatch and the third sub-image 320C and the fourth sub-image 320D mayform the second batch of the second set of sub-images.

In accordance with an embodiment, the circuitry 202 may be configured tofeed or input a set of batches (one at a time) of the selected secondset of sub-images to the neural network model 104 for detection of theone or more objects (e.g., the one or more vehicle license plates118A-118D). For example, the first batch of the second sub-images may befed to the neural network model 104 for detection of the first vehiclelicense plate 118A and the fourth vehicle license plate 118D. Thecircuitry 202 may further detect a first license plate portion 322A forthe first vehicle license plate 118A and a fourth license plate portion322D for the fourth vehicle license plate 118D in the first image 114,as shown in FIG. 3B, based on the application of the trained neuralnetwork model 104 on the first batch of the second set of sub-images.Similarly, for the second iteration of object detection operation, thecircuitry 202 may feed the second batch of second sub-images to theneural network model 104 for detection of the second vehicle licenseplate 118B and the third vehicle license plate 118C. In such case, thecircuitry 202 may detect a second license plate portion 322B for thesecond vehicle license plate 118B and a third license plate portion 322Cfor the third vehicle license plate 118C in the first image 114 based onthe application of the trained neural network model 104 on the secondbatch of the second set of sub-images. Thus, the circuitry 202 maydetect different vehicle license plates portions 322A-322D (i.e. whichmay include the corresponding vehicle license plates 118A-118D) in thefirst image 114 based on the trained neural network model 104 applied onthe second set of sub-images (i.e. selected in batches from the firstset of sub-images 320A-320D). The application of the second set ofsub-images (i.e. with reduced pixel size as compared to actual firstimage 114) to the neural network model 104 may reduce the complexity (ortime consumption) of the neural network model 104 to detect the objects(i.e. license plates) present in the first image 114 capturedoriginally. Further, the second set of sub-images with same pixelcharacteristics (like pixel size, 256*256, or 512*512) selected in onebatch may further reduce the processing complexity of the neural networkmodel 104 to detect the objects (i.e. for example vehicle licenseplates), since the neural network model 104 may process all input secondset of sub-images of equal pixel size in a single batch. Since, theneural network model 104 of the disclosed electronic device 102 do notdirectly process the original first image 114 (which may be capturedwith different pixel characteristics at each instance), the selection ofthe second sub-images in batches may reduce the processing complexityand enhance efficiency of the neural network model 104 for the objectdetection. Therefore, the disclosed electronic device 102 may initiallycontrol the neural network model 104 to detect the one or more boundingboxes from the original first image 114 (i.e. HD or 4K image) andfurther control the same neural network model 104 (or different neuralnetwork model) to detect the one or more objects (i.e. license plates)from or based on the second set of sub-images (i.e. reduced-size croppedimages selected in batches from the detected one or more bounding boxesbased on the probability map information and the applied stochasticoptimization function). The disclosed electronic device 102 may notre-size or re-scale (i.e. reduce size) the originally captured firstimage 114, however select the second set of sub-images, to furtherenhance or maintain the accuracy of the object detection.

In one or more embodiments, the plurality of objects 116 may correspondto one of an animate object or an inanimate object. For example, theplurality of objects 116 may correspond to one or more articlesprocessed on an assembly-line in an industry. In another example, theplurality of objects 116 may correspond to one or more objects (likehuman faces) detected by a surveillance system. In yet another example,the plurality of objects 116 may correspond to one or more obstaclesdetected by a self-driving or autonomous vehicle, and the like.

Although the exemplary scenario 300 is illustrated as discreteoperations, such as 302, 304, 306, 308, 310, and 312, the disclosure isnot so limited. Accordingly, in certain embodiments, such discreteoperations may be further divided into additional operations, combinedinto fewer operations, or eliminated, depending on the particularimplementation without detracting from the essence of the disclosedembodiments.

FIG. 4 is a diagram which depicts a flowchart that illustrates anexemplary method for detection of an object in an image based onstochastic optimization, in accordance with an embodiment of thedisclosure. With reference to FIG. 4, there is shown a flowchart 400.The flow chart is described in conjunction with FIGS. 1, 2, 3A, and 3B.The exemplary method of the flowchart 400 may be executed by theelectronic device 102 or the circuitry 202. The method may start at 402and proceed to 404.

At 404, the one or more bounding boxes 314A-314D, which may include theplurality of objects 116, may be detected in the first image 114 basedon the application of the neural network model 104 on the first image114. In one or more embodiments, the circuitry 202 of the electronicdevice 102 may be configured to detect the one or more bounding boxes314A-314D in the first image 114 based on application of the trainedneural network model 104 on the first image 114. In an embodiment, theone or more bounding boxes 314A-314D may include the plurality ofobjects 116. The detection of the one or more bounding boxes 314A-314Dis described, for example, in FIG. 3A.

At 406, the probability map information may be determined for the firstimage 114 based on the detected one or more bounding boxes 314A-314D. Inone or more embodiments, the circuitry 202 may be configured todetermine the probability map information for the first image 114 basedon the detected one or more bounding boxes 314A-314D which may bedetected based on the application of the neural network model 104 on thefirst image 114. The probability map information may indicate aprobability value for association of each pixel of the first image 114with a portion of one of the plurality of objects 116 in the first image114. In some embodiments, the probability map information may indicatean absence or presence of the portion of an object from the plurality ofobjects 116 at each pixel in the first image 114. The determination ofthe probability map information for the first image 114 is described,for example, in FIG. 3A.

At 408, the region 318 corresponding to the plurality of objects 116 inthe first image 114 may be detected in the first image 114 based on thedetermined probability map information. In one or more embodiments, thecircuitry 202 may be configured to detect the region 318 correspondingto the plurality of objects 116 in the first image 114 based on theprobability map information determined for the first image 114. In anembodiment, the region 318 corresponding to the plurality of objects 116may encompass the one or more bounding boxes 314A-314D or include atleast a portion of each of the one or more bounding boxes 314A-314D ofthe plurality of objects 116. The detection of the region 318corresponding to the plurality of objects 116 in the first image 114 isdescribed, for example, in FIG. 3B.

At 410, the first set of sub-images 320A-320D may be determined from thedetected region 318 based on application of a stochastic optimizationfunction on the determined probability map information. In one or moreembodiments, the circuitry 202 may be configured to determine the firstset of sub-images 320A-320D from the detected region 318 based on theapplication of the stochastic optimization function on the determinedprobability map information for the first image 114. Examples of thestochastic optimization function may include, but are not limited to, acost function, a direct search function, a simultaneous perturbationfunction, a Hill climbing function, a random search function, a Tabusearch function, a Particle Swarm Optimization (PSO) function, an AntColony Optimization function, a simulated annealing function, or agenetic function. In an embodiment, a second size of each of the firstset of sub-images 320A-320D may be lesser than a first size of the firstimage 114. In some embodiments, the circuitry 202 may determine a numberof sub-images of the first set of sub-images 320A-320D, the second sizeof each of the first set of sub-images 320A-320D, or a position of eachof the first set of sub-images 320A-320D in the first image 114, basedon application of the stochastic optimization function on theprobability map information. The determination of the first set ofsub-images is described, for example, in FIG. 3B.

At 412, the plurality of objects 116 may be detected from the second setof sub-images of the first set of sub-images 320A-320D based on theapplication of the neural network model 104 on the second set ofsub-images. In one or more embodiments, the circuitry 202 may beconfigured to detect the plurality of objects 116 from the second set ofsub-images (i.e. selected in batches from the first set of sub-images320A-320D) based on application of the neural network model 104 on thesecond set of sub-images. The detection of the plurality of objects 116(e.g., the one or more vehicle license plates 118 from the second set ofsub-images is described, for example, in FIG. 3B. The control may passto end.

Various embodiments of the disclosure may provide a non-transitorycomputer readable medium and/or storage medium, and/or a non-transitorymachine readable medium and/or storage medium having stored thereon, amachine code and/or a set of instructions executable by a machine, suchas an electronic device, and/or a computer. The set of instructions inthe electronic device may cause the machine and/or computer to performthe operations that comprise determination of probability mapinformation for a first image of a first size, based on application of aneural network model on the first image. The neural network model may betrained to detect one or more objects based on a plurality of imagesassociated with the one or more objects. Further, the probability mapinformation may indicate a probability value for each pixel associatedwith the one or more objects in the first image. The operations mayfurther include detection of a region that may correspond to the one ormore objects in the first image based on the determined probability mapinformation of the first image. The operations may further includedetermination of a first set of sub-images from the detected region,based on application of a stochastic optimization function on thedetermined probability map information. A second size of each of thefirst set of sub-images may be lesser than the first size of the firstimage. Further, the operations may further include detection of the oneor more objects from a second set of sub-images of the first set ofsub-images, based on application of the neural network model on thesecond set of sub-images.

Exemplary aspects of the disclosure may include an electronic device(such as the electronic device 102 in FIG. 1) that may include circuitry(such as the circuitry 202 in FIG. 2) and a memory (such as the memory206 in FIG. 2). The memory 206 of the electronic device 102 may beconfigured to store a neural network model (such as the neural networkmodel 104 in FIG. 1). The neural network model 104 may be trained todetect one or more objects based on a plurality of images associatedwith the one or more objects. The circuitry 202 of the electronic device102 may be configured to determine probability map information for afirst image (such as the first image 114 in FIG. 1), based onapplication of the neural network model 104 on the first image 114. Theprobability map information may indicate a probability value for eachpixel associated with the one or more objects in the first image 114.The circuitry 202 may be further configured to detect of a region thatmay correspond to the one or more objects in the first image 114 basedon the determined probability map information of the first image 114.The circuitry 202 may be further configured to determine a first set ofsub-images from the detected region, based on application of astochastic optimization function on the determined probability mapinformation. A second size of each of the first set of sub-images may belesser than a first size of the first image 114. Further, the circuitry202 may be configured to detect the one or more objects from a secondset of sub-images of the first set of sub-images, based on applicationof the neural network model 104 on the second set of sub-images.

In an embodiment, the electronic device 102 may include an imagecapturing device (such as the image capturing device 106 in FIG. 1) thatmay be configured to capture the first image 114. The circuitry 202 maybe further configured to detect a change in an imaging parameterassociated with the image capturing device 106. Thereafter, thecircuitry 202 may be configured to determine or update the probabilitymap information based on the detected change in the imaging parameter.The imaging parameter associated with the image capturing device 106 mayinclude, but is not limited to, a position parameter associated with theimage capturing device 106, an orientation parameter associated with theimage capturing device 106, a zooming parameter associated with theimage capturing device 106, a type of an image sensor associated withthe image capturing device 106, a pixel size associated with the imagesensor of the image capturing device 106, a lens type associated withthe image capturing device 106, a focal length associated with the imagecapturing device 106 to capture the first image 114, or a geo-locationof the image capturing device 106.

In some embodiments, the circuitry 202 may be configured to control theimage capturing device 106 to capture a second image at a defined timeinterval. Thereafter, the circuitry 202 may determine the probabilitymap information for the captured second image. In accordance with anembodiment, the one or more objects may correspond to one or morelicense plates of one or more vehicles captured in the first image 114.

In accordance with an embodiment, the circuitry 202 may be configured todetect one or more bounding boxes in the first image 114 based onapplication of the neural network model 104 on the first image 114. Eachof the one or more bounding boxes may include the one or more objects.In an embodiment, the circuitry 202 may be further configured todetermine the probability map information for the first image 114 basedon the detected one or more bounding boxes in the first image 114.

In accordance with an embodiment, the neural network model 104 mayinclude, but is not limited to, an artificial neural network (ANN), aconvolutional neural network (CNN), a CNN-recurrent neural network(CNN-RNN), Region-CNN (R-CNN), Fast R-CNN, Faster R-CNN, a Long ShortTerm Memory (LSTM) network based RNN, a combination of CNN and ANN, acombination of LSTM and ANN, a gated recurrent unit (GRU)-based RNN, adeep Bayesian neural network, a Generative Adversarial Network (GAN), adeep learning based object detection model, a feature-based objectdetection model, an image segmentation based object detection model, ablob analysis-based object detection model, a “you look only once”(YOLO) object detection model, or a single-shot multi-box detector (SSD)based object detection model. In accordance with an embodiment, thestochastic optimization function may include, but not limited to, a costfunction, a direct search function, a simultaneous perturbationfunction, a Hill climbing function, a random search function, a Tabusearch function, a Particle Swarm Optimization (PSO) function, an AntColony Optimization function, a simulated annealing function, or agenetic function.

In accordance with an embodiment, the circuitry 202 may be configured todetermine at least one of a number of sub-images of the first set ofsub-images, a size of each of the first set of sub-images, or a positionof each of the first set of sub-images in the first image, based on theapplication of the stochastic optimization function on the determinedprobability map information.

The present disclosure may be realized in hardware, or a combination ofhardware and software. The present disclosure may be realized in acentralized fashion, in at least one computer system, or in adistributed fashion, where different elements may be spread acrossseveral interconnected computer systems. A computer system or otherapparatus adapted to carry out the methods described herein may besuited. A combination of hardware and software may be a general-purposecomputer system with a computer program that, when loaded and executed,may control the computer system such that it carries out the methodsdescribed herein. The present disclosure may be realized in hardwarethat comprises a portion of an integrated circuit that also performsother functions.

The present disclosure may also be embedded in a computer programproduct, which comprises all the features that enable the implementationof the methods described herein, and which when loaded in a computersystem is able to carry out these methods. While the present disclosurehas been described with reference to certain embodiments, it will beunderstood by those skilled in the art that various changes may be madeand equivalents may be substituted without departure from the scope ofthe present disclosure. In addition, many modifications may be made toadapt a particular situation or material to the teachings of the presentdisclosure without departing from its scope. Therefore, it is intendedthat the present disclosure not be limited to the particular embodimentdisclosed, but that the present disclosure will include all embodimentsthat fall within the scope of the appended claims.

What is claimed is:
 1. An electronic device, comprising: circuitryconfigured to: determine probability map information for a first imageof a first size, based on application of a neural network model on thefirst image, wherein the neural network model is trained to detect atleast one object based on a plurality of images associated with the atleast one object, the determined probability map information indicates aprobability value for each pixel of a plurality of pixels of the firstimage, and the plurality of pixels is associated with the at least oneobject in the first image; detect a region that corresponds to the atleast one object in the first image based on the determined probabilitymap information of the first image, wherein the detected region includesa set of pixels of the plurality of pixels, and the probability valuefor each pixel of the set of pixels exceeds a threshold value; determinea first set of sub-images from the detected region, based on applicationof a stochastic optimization function on the determined probability mapinformation, wherein a second size of each of the first set ofsub-images is less than the first size of the first image; determine atleast one of a number of sub-images of the first set of sub-images, thesecond size of each of the first set of sub-images, or a position ofeach of the first set of sub-images in the first image, based on theapplication of the stochastic optimization function on the determinedprobability map information; crop the first set of sub-images from thefirst image based on the determined at least one of the number ofsub-images of the first set of sub-images, the second size of each ofthe first set of sub-images, or the position of each of the first set ofsub-images in the first image; select a second set of sub-images fromthe cropped first set of sub-images; and detect the at least one objectfrom the second set of sub-images of the first set of sub-images, basedon application of the neural network model on the second set ofsub-images.
 2. The electronic device according to claim 1, furthercomprising an image capturing device configured to capture the firstimage, wherein the circuitry is further configured to: detect a changein an imaging parameter associated with the image capturing device; anddetermine the probability map information based on the detected changein the imaging parameter.
 3. The electronic device according to claim 2,wherein the imaging parameter associated with the image capturing devicecomprises at least one of a position parameter associated with the imagecapturing device, an orientation parameter associated with the imagecapturing device, a zooming parameter associated with the imagecapturing device, a type of an image sensor associated with the imagecapturing device, a pixel size associated with the image sensor of theimage capturing device, a lens type associated with the image capturingdevice, a focal length associated with the image capturing device tocapture the first image, or a geo-location of the image capturingdevice.
 4. The electronic device according to claim 2, wherein thecircuitry is further configured to: control the image capturing deviceto capture a second image at a specific time interval; and determine theprobability map information for the captured second image.
 5. Theelectronic device according to claim 1, wherein the at least one objectcorresponds to a license plate of at least one vehicle in the firstimage.
 6. The electronic device according to claim 1, wherein the neuralnetwork model comprises one of an artificial neural network (ANN), aconvolutional neural network (CNN), a CNN-recurrent neural network(CNN-RNN), Region-CNN (R-CNN), Fast R-CNN, Faster R-CNN, a Long ShortTerm Memory (LSTM) network based RNN, a combination of CNN and ANN, acombination of LSTM and ANN, a gated recurrent unit (GRU)-based RNN, adeep Bayesian neural network, a Generative Adversarial Network (GAN), adeep learning based object detection model, a feature-based objectdetection model, an image segmentation based object detection model, ablob analysis-based object detection model, a “you look only once”(YOLO) object detection model, or a single-shot multi-box detector (SSD)based object detection model.
 7. The electronic device according toclaim 1, wherein the stochastic optimization function comprises one of acost function, a direct search function, a simultaneous perturbationfunction, a Hill climbing function, a random search function, a Tabusearch function, a Particle Swarm Optimization (PSO) function, an AntColony Optimization function, a simulated annealing function, or agenetic function.
 8. The electronic device according to claim 1, whereinthe circuitry is further configured to: detect at least one bounding boxin the first image based on the application of the neural network modelon the first image, wherein the at least one bounding box includes theat least one object; and determine the probability map information forthe first image of the first size based on the detected at least onebounding box in the first image.
 9. A method, comprising: in anelectronic device: determining probability map information for a firstimage of a first size, based on application of a neural network model onthe first image, wherein the neural network model is trained to detectat least one object based on a plurality of images associated with theat least one object, the determined probability map informationindicates a probability value for each pixel of a plurality of pixels ofthe first image, and the plurality of pixels is associated with the atleast one object in the first image; detecting a region that correspondsto the at least one object in the first image based on the determinedprobability map information of the first image, wherein the detectedregion includes a set of pixels of the plurality of pixels, and theprobability value for each pixel of the set of pixels exceeds athreshold value; determining a first set of sub-images from the detectedregion, based on application of a stochastic optimization function onthe determined probability map information, wherein a second size ofeach of the first set of sub-images is less than the first size of thefirst image; determining at least one of a number of sub-images of thefirst set of sub-images, the second size of each of the first set ofsub-images, or a position of each of the first set of sub-images in thefirst image, based on the application of the stochastic optimizationfunction on the determined probability map information; cropping thefirst set of sub-images from the first image based on the determined atleast one of the number of sub-images of the first set of sub-images,the second size of each of the first set of sub-images, or the positionof each of the first set of sub-images in the first image; selecting asecond set of sub-images from the cropped first set of sub-images; anddetecting the at least one object from the second set of sub-images ofthe first set of sub-images, based on application of the neural networkmodel on the second set of sub-images.
 10. The method according to claim9, further comprising: detecting a change in an imaging parameterassociated with an image capturing device of the electronic device; anddetermining the probability map information based on the detected changein the imaging parameter.
 11. The method according to claim 10, whereinthe imaging parameter associated with the image capturing devicecomprises at least one of a position parameter associated with the imagecapturing device, an orientation parameter associated with the imagecapturing device, a zooming parameter associated with the imagecapturing device, a type of an image sensor associated with the imagecapturing device, a pixel size associated with the image sensor of theimage capturing device, a lens type associated with the image capturingdevice, a focal length associated with the image capturing device tocapture the first image, or a geo-location of the image capturingdevice.
 12. The method according to claim 10, further comprising:controlling the image capturing device to capture a second image at aspecific time interval; and determining the probability map informationfor the captured second image.
 13. The method according to claim 9,wherein the at least one object corresponds to a license plate of atleast one vehicle in the first image.
 14. The method according to claim9, wherein the neural network model comprises one of an artificialneural network (ANN), a convolutional neural network (CNN), aCNN-recurrent neural network (CNN-RNN), Region-CNN (R-CNN), Fast R-CNN,Faster R-CNN, a Long Short Term Memory (LSTM) network based RNN, acombination of CNN and ANN, a combination of LSTM and ANN, a gatedrecurrent unit (GRU)-based RNN, a deep Bayesian neural network, aGenerative Adversarial Network (GAN), a deep learning based objectdetection model, a feature-based object detection model, an imagesegmentation based object detection model, a blob analysis-based objectdetection model, a “you look only once” (YOLO) object detection model,or a single-shot multi-box detector (SSD) based object detection model.15. The method according to claim 9, wherein the stochastic optimizationfunction comprises one of a cost function, a direct search function, asimultaneous perturbation function, a Hill climbing function, a randomsearch function, a Tabu search function, a Particle Swarm Optimization(PSO) function, an Ant Colony Optimization function, a simulatedannealing function, or a genetic function.
 16. The method according toclaim 9, further comprising: detecting at least one bounding box in thefirst image based on the application of the neural network model on thefirst image, wherein the at least one bounding box includes the at leastone object; and determining the probability map information for thefirst image of the first size based on the detected at least onebounding box in the first image.
 17. A non-transitory computer-readablemedium having stored thereon, computer-executable instructions that whenexecuted by an electronic device, causes the electronic device toexecute operations, the operations comprising: determining probabilitymap information for an image of a first size, based on application of aneural network model on the image, wherein the neural network model istrained to detect at least one object based on a plurality of imagesassociated with the at least one object, the determined probability mapinformation indicates a probability value for each pixel of a pluralityof pixels of the image, and the plurality of pixels is associated withthe at least one object in the image; detecting a region thatcorresponds to the at least one object in the image based on thedetermined probability map information of the image, wherein thedetected region includes a set of pixels of the plurality of pixels, andthe probability value for each pixel of the set of pixels exceeds athreshold value; determining a first set of sub-images from the detectedregion, based on application of a stochastic optimization function onthe determined probability map information, wherein a second size ofeach of the first set of sub-images is less than the first size of theimage; determining at least one of a number of sub-images of the firstset of sub-images, the second size of each of the first set ofsub-images, or a position of each of the first set of sub-images in theimage, based on the application of the stochastic optimization functionon the determined probability map information; cropping the first set ofsub-images from the image based on the determined at least one of thenumber of sub-images of the first set of sub-images, the second size ofeach of the first set of sub-images, or the position of each of thefirst set of sub-images in the image; selecting a second set ofsub-images from the cropped first set of sub-images; and detecting theat least one object from a second set of sub-images of the first set ofsub-images, based on application of the neural network model on thesecond set of sub-images.
 18. The non-transitory computer-readablemedium according to claim 17, wherein the at least one objectcorresponds to a license plate of at least one vehicle in the image.